A trajectory-based approach to understand the factors associated with persistent depressive symptoms in primary care

Background sion screening in primary care yields high numbers. Knowledge of how depressive symptoms change over time is limited, making decisions about type, intensity, frequency and length of treatment and follow-up difficult. This study is aimed to identify depressive symptom trajectories and associated socio–demographic, co-morbidity, health service use and treatment factors to inform clinical care. s ople scoring 16 or more on the CES-D recruited from 30 randomly selected Australian family practices. Depressive symptoms are measured using PHQ-9 at 3, 6, 9 and 12 months. s mixture modelling identified a five-class trajectory model as the best fitting (lowest Bayesian Information Criterion): three groups were static (mild (n=532), moderate (n=138) and severe (n=69)) and two were dynamic (decreasing severity (n=32) and increasing severity (n=18)). The mild symptom trajectory was the most common (n=532). The severe symptom trajectory group (n=69) differed significantly from the mild symptom trajectory group on most variables. The severe and moderate groups were characterised by high levels of disadvantage, abuse, morbidity and disability. Decreasing and increasing severity trajectory classes were similar on most variables. tions only cohort, self-report measures. sions ymptom trajectories remained static, suggesting that depression, as it presents in primary care, is not always an episodic disorder. The findings indicate future directions for building prognostic models to distinguish those who are likely to have a mild course from those who are likely to follow more severe trajectories. Determining appropriate clinical responses based upon a likely depression course requires further research.